Interrupt me Politely: Recommending Products and Services by Joining Human Conversation

Boris Galitsky, Dmitry Ilvovsky


Abstract
We propose a novel way of conversational recommendation, where instead of asking questions to the user to acquire their preferences; the recommender tracks their conversation with other people, including customer support agents (CSA), and joins the conversation only when it is time to introduce a recommendation. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and argumentation analyses, as well as dialogue management techniques to compute a recommendation for a product and service that is needed by the customer, as inferred from the conversation. A special case of such conversations is considered where the customer raises his problem with CSA in an attempt to resolve it, along with receiving a recommendation for a product with features addressing this problem. We evaluate performance of RJC is in a number of human-human and human-chat bot dialogues, and demonstrate that RJC is an efficient and less intrusive way to provide high relevance and persuasive recommendations.
Anthology ID:
2020.ecomnlp-1.4
Volume:
Proceedings of Workshop on Natural Language Processing in E-Commerce
Month:
Dec
Year:
2020
Address:
Barcelona, Spain
Venues:
COLING | EcomNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–42
Language:
URL:
https://www.aclweb.org/anthology/2020.ecomnlp-1.4
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ecomnlp-1.4.pdf